Influence of Irrigation Regimes, Frequency and Mulching on Productivity of Bt. Cotton

Prajapati, G. V.

doi:10.29321/maj.2017.000052

Cited by 3 publications

(3 citation statements)

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“…The better plant vigor under SDI over SF in the present findings is in close proximity with those of Yadav and Chauhan (2016), who observed taller plants under SDI over SF. Furthermore, SF not only recorded shorter plants in line with Prajapati and Subbaiah (2018) but also lesser biomass accumulation (2,138 g m -2 ) in comparison with various SDF/SSDF treatments under 80%/ 100% ETc, elucidating that applied water and nitrogen could not be efficiently utilized (Table 5).…”

Section: Growth Parameters and Biomass Accumulation By Cottonmentioning

confidence: 94%

Sub-surface drip fertigation improves seed cotton yield and monetary returns

Singh

et al. 2022

Front. Plant Sci.

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Surface flood (SF) method is used to irrigate cotton in India, which results in huge wastage of water besides leaching of nutrients. This necessitates the adoption of efficient management strategies to save scarce water without compromising the yield. Therefore, a 2-year field investigation was conducted under two climatic regimes (Faridkot and Abohar) to study the effect of sub-surface drip fertigation (SSDF) on seed cotton yield (SCY), water productivity, nitrogen use efficiency (NUE), and economic parameters in comparison with SF and surface drip fertigation (SDF). The field experiment had a total of eight treatments arranged in a randomized complete block design. Three levels of sub-surface drip irrigation [(SSDI); i.e., 60%, 80%, and 100% of crop evapotranspiration (ETc)] and two N fertigation levels [100% recommended dose of nitrogen (RDN; i.e., 112.5 kg N ha-1) and 75% RDN] made up six treatments, while SF (Control 1) and SDF at 80% ETc (Control 2), both with 100% of RDN, served as the controls. Among irrigation regimes, the SSDI levels of 80% ETc and 100% ETc recorded 18.7% (3,240 kg ha-1) and 21.1% (3,305 kg ha-1) higher SCY compared with SF (2,728 kg ha-1). Water use efficiency under SF (57.0%) was reduced by 34.2%, 40.8%, and 38.2% compared with SSDI’s 60 (76.5%), 80 (80.3%), and 100% ETc (78.8%), respectively. Among fertigation levels, NUE was higher by 19.2% under 75% (34.1 kg SCY kg-1 N) over 100% RDN (28.6 kg SCY kg-1 N), but later it also registered 11.9% higher SCY, indicating such to be optimum for better productivity. SSDF at 80% ETc along with 112.5 kg N ha-1 recorded 26.6% better SCY (3455 kg ha-1) and 18.5% higher NUE (30.7 kg SCY kg-1 N) over SF. These findings demonstrate that the application of SSDF could save irrigation water, enhance SCY, and improve the farmers’ returns compared with SF. Therefore, in northwestern India, SSDF at 80% ETc along with 112.5 kg N ha-1 could be a novel water-savvy concept which would be immensely helpful in enhancing cotton productivity.

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Section: Growth Parameters and Biomass Accumulation By Cottonmentioning

confidence: 94%

Sub-surface drip fertigation improves seed cotton yield and monetary returns

Singh

et al. 2022

Front. Plant Sci.

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“…Irrigation water demand was also estimated using Pan ET method using adjusted FAO K c for respective treatments and compared with water requirement estimated using sensor based ET a values depicted in Table 2. It indicated that cumulative irrigation water estimated by Pan ET c approach was higher of 16.1 per cent and 13.3 per cent than sensor based irrigation at I 1 and I 2 respectively (Prajapati and Subbaiah, 2018).…”

Section: Determination Of K C Curves K C For No Mulch : K C Ini For Drip Irrigated Cotton Without Mulchmentioning

confidence: 91%

Crop coefficients of Bt. cotton under variable moisture regimes and mulching

Prajapati¹,

Subbaiah²

2021

J. Agrometeorol.

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An experiment was undertaken for two years (2013-14 and 2014-15) on sandy loam soils of Junagadh (21°30’ N, 70°27’ E) to determine crop coefficients of Bt. cotton under three mulch treatments (M1- silver black plastic mulch, M2- biodegradable plastic mulch, M3- wheat straw mulch and C - control) with two irrigation regimes (I1- 1.0 IW/ETc, I2-0.8 IW/ETc). Actual evapotranspiration was estimated using soil moisture sensors. Results revealed that adjusted FAO Kc predicts higher value than sensor based Kc values at both irrigation regimes. Silver black plastic mulch recorded lower crop coefficient values at all growth stage of Bt. cotton compared to biodegradable plastic mulch, wheat straw mulch and control. Sensor based Kc ini and Kc-devvalues of silver black plastic mulch observed 72.2, 29.0 and 66.9 and 14.8 per cent lower than control at 1.0 IW/ETc and 0.8 IW/ETc respectively. Overestimated adjusted FAO Kc values caused a loss of 78.1mm and 66.5mm of precious water at 1.0 IW/ETc and 0.8 IW/ETc respectively. This study admonishes blind adoption of published FAO Kc curves for mulch conditions.

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“…Cotton is widely grown as a principal source of vegetable oil and staple fiber, and Gossypium hirsutum is the most cultivated cotton species in the world [1]. It is one of the most important fibers and cash crops of India and plays a dominant role in the industry and the agricultural economy of the country [2]. Cotton is an important agricultural product; however, it suffers from water deficiency, and the reliance on robust varieties well adapted to the rigorous conditions of drought warrants a robust understanding of gene action [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models

et al. 2022

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Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) were obtained from an experiment conducted by the Central Institute of Cotton Research (CICR), Sirsa, Haryana (India) during the Kharif season of the year 2018–2019. The machine learning (ML) classifiers/models, namely k-nearest neighbor (KNN), Classification and Regression Tree (CART), C4.5, Naïve Bayes, random forest (RF), bagging, and boosting were considered for cotton genotypes classification. The performance of these ML classifiers was compared to each other along with the linear discriminant analysis (LDA) and logistic regression. The holdout method was used for cross-validation with an 80:20 ratio of training and testing data. The results of the appraisal based on hold-out cross-validation showed that the RF and AdaBoost performed very well, having only two misclassifications with the same accuracy of 97.26% and the error rate of 2.74%. The LDA classifier performed the worst in terms of accuracy, with nine misclassifications. The other performance measures, namely sensitivity, specificity, precision, F1 score, and G-mean, were all together used to find out the best ML classifier among all those considered. Moreover, the RF and AdaBoost algorithms had the highest value of all the performance measures, with 96.97% sensitivity and 97.50% specificity. Thus, these models were found to be the best in classifying the low- and high-yielding cotton genotypes.

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Influence of Irrigation Regimes, Frequency and Mulching on Productivity of Bt. Cotton

Cited by 3 publications

References 0 publications

Sub-surface drip fertigation improves seed cotton yield and monetary returns

Sub-surface drip fertigation improves seed cotton yield and monetary returns

Crop coefficients of Bt. cotton under variable moisture regimes and mulching

Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models

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